Investigating Gender and Racial Biases in DALL-E Mini Images
Open accessAbstract
Generative artificial intelligence systems based on transformers, including both text generators such as GPT-4 and image generators such as DALL-E 3, have recently entered the popular consciousness. These tools, while impressive, are liable to reproduce, exacerbate, and reinforce extant human social biases, such as gender and racial biases. In this article, we systematically review the extent to which DALL-E Mini suffers from this problem. In line with the Model Card published alongside DALL-E Mini by its creators, we find that the images it produces tend to represent dozens of different occupations as populated either solely by men (e.g., pilot, builder, plumber) or solely by women (e.g., hairdresser, receptionist, dietitian). In addition, the images DALL-E Mini produces tend to represent most occupations as populated primarily or solely by White people (e.g., farmer, painter, prison officer, software engineer) and very few by non-White people (e.g., pastor, rapper). These findings suggest that exciting new AI technologies should be critically scrutinized and perhaps regulated before they are unleashed on society.
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Generative AI outputs tend to reproduce normative views and mirror societal biases for marginalized groups, including people with disabilities.
“Generative AI outputs tend to reproduce normative views and mirror societal biases for marginalized groups [15, 21, 36, 41, 42, 45, 53, 66, 72, 91], including PwD [10, 12, 39, 48, 54, 62, 77, 113].”
From Engaging Communities Meaningfully in Defining Disability Representation for AI Image Generation (2026) · cites this paper as [21]
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Cites
Rudin, C. (2019) · Nature Machine Intelligence · doi:10.1038/s42256-019-0048-x · Cited by 10,561 · see citing works
Dissecting racial bias in an algorithm used to manage the health of populations
Obermeyer, Z., Powers, B., Vogeli, C., & Mullainathan, S. (2019) · Science · doi:10.1126/science.aax2342 · Cited by 7,137 · cited 1× · see citing works
“…in [17]), far from realizing the espoused ideal of impartiality, AI bias is both pervasive and pernicious: implicating everything from unequal access to health care [61, 65] and education [53, 69]; to reduced employment prospects [4, 67] and racially skewed rates of (re)incarceration [46, 79].”
BackgroundPeeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)
Adadi, A., & Berrada, M. (2018) · IEEE Access · doi:10.1109/access.2018.2870052 · Cited by 6,294 · see citing works
International Labour Organization
(2022) · Permanent missions to the United Nations · doi:10.18356/9789210018289c229 · Cited by 4,312 · see citing works
The ethics of algorithms: Mapping the debate
Mittelstadt, B. D., Allo, P., Taddeo, M., Wachter, S., & Floridi, L. (2016) · Big Data & Society · doi:10.1177/2053951716679679 · Cited by 3,432 · cited 1× · see citing works
“Of particular concern is the issue of bias 6 [7, 57, 76]Ðthe propensity of such systems to relect, entrench, and reinforce harmful stereotypes and prejudice that exist in society writ large [49].”
BackgroundWeapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy
Doyle, T. (2017) · The Information Society · doi:10.1080/01972243.2017.1354593 · Cited by 3,110 · see citing works
Model Cards for Model Reporting
Mitchell, M., Wu, S., Zaldivar, A., Barnes, P., Vasserman, L., Hutchinson, B., Spitzer, E., Raji, I. D., & Gebru, T. (2019) · Proceedings of the Conference on Fairness, Accountability, and Transparency · doi:10.1145/3287560.3287596 · Cited by 2,681 · cited 1× · see citing works
“In contrast, nurses, secretaries or assistants are typically women, often white as well. ž They further highlight in their Model Card [56]Ða report on the limitations and dangers of the DALL-E Mini modelsÐthat łinitial testing demonstrates that they may generate images that contain negative…”
BackgroundTaming Transformers for High-Resolution Image Synthesis
Esser, P., Rombach, R., & Ommer, B. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.01268 · Cited by 2,514 · see citing works
Amazon Scraps Secret AI Recruiting Tool that Showed Bias against Women *
Dastin, J. (2022) · Ethics of Data and Analytics · doi:10.1201/9781003278290-44 · Cited by 1,165 · see citing works
The accuracy, fairness, and limits of predicting recidivism
Dressel, J., & Farid, H. (2018) · Science Advances · doi:10.1126/sciadv.aao5580 · Cited by 1,071 · see citing works
Mitigating bias in algorithmic hiring: evaluating claims and practices
Raghavan, M., Barocas, S., Kleinberg, J., & Levy, K. (2020) · Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency · doi:10.1145/3351095.3372828 · Cited by 845 · see citing works
Ethical Implications and Accountability of Algorithms
Martin, K. (2019) · Journal of Business Ethics · doi:10.1007/s10551-018-3921-3 · Cited by 757 · see citing works
Algorithmic injustice: A relational ethics approach
Birhane, A. (2021) · Patterns · doi:10.1016/j.patter.2021.100205 · Cited by 707 · cited 3× · see citing works
“Gaining a clearer understanding of the extent to which multi-modal generative models are biased, what sorts of biases they perpetuate, and who sufers most at the hands of biased representation is of critical import [12, 73].”
BackgroundConceptual 12M: Pushing Web-Scale Image-Text Pre-Training To Recognize Long-Tail Visual Concepts
Changpinyo, S., Sharma, P. K., Ding, N., & Soricut, R. (2021) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr46437.2021.00356 · Cited by 626 · see citing works
The growing energy footprint of artificial intelligence
de Vries, A. (2023) · Joule · doi:10.1016/j.joule.2023.09.004 · Cited by 597 · see citing works
Data and its (dis)contents: A survey of dataset development and use in machine learning research
Paullada, A., Raji, I. D., Bender, E. M., Denton, E., & Hanna, A. (2021) · Patterns · doi:10.1016/j.patter.2021.100336 · Cited by 548 · see citing works
Artificial intelligence and algorithmic bias: implications for health systems
Panch, T., Mattie, H., & Atun, R. (2019) · Journal of Global Health · doi:10.7189/jogh.09.020318 · Cited by 533 · see citing works
Unequal Representation and Gender Stereotypes in Image Search Results for Occupations
Kay, M., Matuszek, C., & Munson, S. A. (2015) · Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems · doi:10.1145/2702123.2702520 · Cited by 509 · see citing works
Sweeney, L. (2013) · Queue · doi:10.1145/2460276.2460278 · Cited by 477 · see citing works
Can Gender-Fair Language Reduce Gender Stereotyping and Discrimination?
Sczesny, S., Formanowicz, M., & Moser, F. (2016) · Frontiers in Psychology · doi:10.3389/fpsyg.2016.00025 · Cited by 404 · see citing works
The ethics of algorithms: key problems and solutions
Tsamados, A., Aggarwal, N., Cowls, J., Morley, J., Roberts, H., Taddeo, M., & Floridi, L. (2022) · AI & SOCIETY · doi:10.1007/s00146-021-01154-8 · Cited by 384 · see citing works
Easily Accessible Text-to-Image Generation Amplifies Demographic Stereotypes at Large Scale
Bianchi, F., Kalluri, P., Durmus, E., Ladhak, F., Cheng, M., Nozza, D., Hashimoto, T., Jurafsky, D., Zou, J., & Caliskan, A. (2023) · 2023 ACM Conference on Fairness, Accountability, and Transparency · doi:10.1145/3593013.3594095 · Cited by 342 · see citing works
Bias in Bios: A Case Study of Semantic Representation Bias in a High-Stakes Setting
De-Arteaga, M., Romanov, A., Wallach, H., Chayes, J., Borgs, C., Chouldechova, A., Geyik, S., Kenthapadi, K., & Kalai, A. T. (2019) · Proceedings of the Conference on Fairness, Accountability, and Transparency · doi:10.1145/3287560.3287572 · Cited by 337 · see citing works
The Ugly Truth About Ourselves and Our Robot Creations: The Problem of Bias and Social Inequity
Howard, A., & Borenstein, J. (2018) · Science and Engineering Ethics · doi:10.1007/s11948-017-9975-2 · Cited by 337 · see citing works
Manzini, T., Chong, L. Y., Black, A. W., & Tsvetkov, Y. (2019) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n19-1062 · Cited by 284 · see citing works
DALL-EVAL: Probing the Reasoning Skills and Social Biases of Text-to-Image Generation Models
Cho, J., Zala, A., & Bansal, M. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00283 · Cited by 199 · see citing works
Evaluating the Underlying Gender Bias in Contextualized Word Embeddings
Basta, C., Costa-jussà, M. R., & Casas, N. (2019) · Proceedings of the First Workshop on Gender Bias in Natural Language Processing · doi:10.18653/v1/w19-3805 · Cited by 189 · see citing works
Guo, W., & Caliskan, A. (2021) · Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society · doi:10.1145/3461702.3462536 · Cited by 180 · see citing works
Image Representations Learned With Unsupervised Pre-Training Contain Human-like Biases
Steed, R., & Caliskan, A. (2021) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3442188.3445932 · Cited by 167 · see citing works
Assessing Cross-Cultural Alignment between ChatGPT and Human Societies: An Empirical Study
Cao, Y., Zhou, L., Lee, S., Cabello, L., Chen, M., & Hershcovich, D. (2023) · Proceedings of the First Workshop on Cross-Cultural Considerations in NLP (C3NLP) · doi:10.18653/v1/2023.c3nlp-1.7 · Cited by 163 · see citing works
Santelices, M. V., & Wilson, M. R. (2010) · Harvard Educational Review · doi:10.17763/haer.80.1.j94675w001329270 · Cited by 146 · see citing works
Liao, S., & Huebner, B. (2020) · Philosophy and Phenomenological Research · doi:10.1111/phpr.12701 · Cited by 141 · see citing works
Guo, L. N., Lee, M. S., Kassamali, B., Mita, C., & Nambudiri, V. E. (2022) · Journal of the American Academy of Dermatology · doi:10.1016/j.jaad.2021.06.884 · Cited by 122 · see citing works
Androcentric Reporting of Gender Differences in APA Journals: 1965–2004
Hegarty, P. V., & Buechel, C. (2006) · Review of General Psychology · doi:10.1037/1089-2680.10.4.377 · Cited by 114 · see citing works
AI Ethics in Predictive Policing: From Models of Threat to an Ethics of Care
Asaro, P. (2019) · IEEE Technology and Society Magazine · doi:10.1109/mts.2019.2915154 · Cited by 110 · see citing works
Propagation of societal gender inequality by internet search algorithms
Vlasceanu, M., & Amodio, D. M. (2022) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2204529119 · Cited by 109 · see citing works
Diagnosing Gender Bias in Image Recognition Systems
Schwemmer, C., Knight, C. R., Bello-Pardo, E. D., Oklobdzija, S. N., Schoonvelde, M., & Lockhart, J. W. (2020) · Socius Sociological Research for a Dynamic World · doi:10.1177/2378023120967171 · Cited by 91 · see citing works
Misinformation reloaded? Fears about the impact of generative AI on misinformation are overblown
Simon, F. M., Altay, S., & Mercier, H. (2023) · Harvard Kennedy School Misinformation Review · doi:10.37016/mr-2020-127 · Cited by 85 · cited 1× · see citing works
“Future work will look at eforts in this area, for example how DALL-E 3’s online API approaches the issue of debiasing output.”
BackgroundTowards gender equity in artificial intelligence and machine learning applications in dermatology
Lee, M. S., Guo, L. N., & Nambudiri, V. E. (2021) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocab113 · Cited by 56 · see citing works
Distributional term representations
Lavelli, A., Sebastiani, F., & Zanoli, R. (2004) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/1031171.1031284 · Cited by 52 · see citing works
Hiring Fairly in the Age of AlgorithmsPreprint
Langenkamp, M., Costa, A., & Cheung, C. (2019) · SSRN Electronic Journal · doi:10.2139/ssrn.3723046 · Cited by 38 · see citing works
Ghosh, S., & Caliskan, A. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.465 · Cited by 32 · see citing works
T2IAT: Measuring Valence and Stereotypical Biases in Text-to-Image Generation
Wang, J., Liu, X., Di, Z., Liu, Y., & Wang, X. E. (2023) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2023.findings-acl.160 · Cited by 26 · see citing works
Bias among managers: Its prevalence across a decade and comparison across occupations
Cunningham, G. B., & Cunningham, H. R. (2022) · Frontiers in Psychology · doi:10.3389/fpsyg.2022.1034712 · Cited by 15 · see citing works
Dayma, B., Patil, S., Cuenca, P., Saifullah, K., Abraham, T., Lê Khắc, P., Melas, L., & Ghosh, R. (2021) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.5146399 · Cited by 13 · see citing works
For minorities, biased AI algorithms can damage almost every part of life
Adib-Moghaddam, A. (2023) · doi:10.64628/ab.4ken7x5hs · Cited by 1 · see citing works
Flagged first, then by citations
1 of the 47 references couldn’t be matched to a known paper.
Research works printed but not linked: 8
- I Ajunwa, S A Friedler, C Scheidegger, and S Venkatasubramanian. 2016. Hiring by algorithm: predicting and preventing disparate impact. (2016). http://sorelle.friedler.net/papers/SSRN-id2746078.pdf
- Tolga Bolukbasi, Kai-Wei Chang, James Zou, Venkatesh Saligrama, and Adam Kalai. [n. d.]. Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings. Adv. Neural Inf. Process. Syst. ([n. d.]). https://proceedings.neurips.cc/paper/2016/ile/ a486cd07e4ac3d270571622f4f316ec5-Paper.pdf
- Tom B Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M Ziegler, Jefrey Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei. 2020. Language Models are Few-Shot Learners. (May 2020). arXiv:2005.14165 [cs.CL] http://arxiv.org/abs/2005.14165 · arXiv
- Chris Dulhanty and Alexander Wong. 2019. Auditing ImageNet: Towards a Model-driven Framework for Annotating Demographic Attributes of Large-Scale Image Datasets. arXiv:1905.01347 [cs.LG] · arXiv
- Alexandra Sasha Luccioni, Christopher Akiki, Margaret Mitchell, and Yacine Jernite. 2023. Stable Bias: Analyzing Societal Representations in Difusion Models. arXiv:2303.11408 [cs.CY] · arXiv
- Sheilla Njoto, Marc Cheong, Reeva Lederman, Aidan McLoughney, Leah Ruppanner, and Anthony Wirth. 2022. Gender Bias in AI Recruitment Systems: A Sociological-and Data Science-based Case Study. In Proceedings of the 2022 IEEE International Symposium on Technology and Society (ISTAS).
- Saiya Umoja Noble. 2018. Algorithms of Oppression: How Search Engines Reinforce Racism. NYU Press.
- Yi Chern Tan and L Elisa Celis. 2019. Assessing Social and Intersectional Biases in Contextualized Word Representations. arXiv (Nov. 2019). arXiv:1911.01485 [cs.CL] http://arxiv.org/abs/1911.01485 · arXiv
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Katirai, A., García, N., Ide, K., Nakashima, Y., & Kishimoto, A. (2024) · AI and Ethics · doi:10.1007/s43681-024-00517-3 · Cited by 17 · see citing works
Abrar, A., Oeshy, N. T., Kabir, M., & Ananiadou, S. (2025) · AI & Society · doi:10.1007/s00146-025-02721-z · Cited by 15 · see citing works
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Spiegler, S. V., Hoda, R., & Pant, A. (2025) · Technology in Society · doi:10.1016/j.techsoc.2025.103109 · Cited by 11 · see citing works
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Alfano, M., Abedin, E., Reimann, R., Ferreira, M., & Cheong, M. (2024) · Ethics and Information Technology · doi:10.1007/s10676-024-09760-y · Cited by 11 · see citing works
Self-citationNakano, T., Shimari, K., Kula, R. G., Treude, C., Cheong, M., & Matsumoto, K. (2024) · IEEE International Conference on Software Maintenance (ICSM) · doi:10.1109/icsme58944.2024.00063 · Cited by 10 · see citing works
Self-citationImagining the Far East: Exploring Perceived Biases in AI-Generated Images of East Asian Women
Lan, X., An, J., Guo, Y., Chiyou, T., Cai, X., & Zhang, J. (2025) · Proceedings of the Extended Abstracts of the CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706599.3719678 · Cited by 8 · see citing works
InvDiff: Invariant Guidance for Bias Mitigation in Diffusion Models
Hou, M., Wu, Y., Xu, C., Huang, Y.-H., Bai, C., Wu, L., & Bian, J. (2025) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3690624.3709165 · Cited by 7 · see citing works
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Record, N. R., Tupper, B., Douyon, K., Drakopulos, L., Vera, L. A., & Evanilla, J. (2025) · Environmental Science & Technology · doi:10.1021/acs.est.4c10838 · Cited by 6 · see citing works
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Bauer, N. F. (2025) · Springer eBooks · doi:10.1007/978-3-658-46344-1_2 · Cited by 5 · see citing works
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Foka, A. F. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-92089-9_9 · Cited by 4 · see citing works
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Turner, J. D., & Danridge-Boston, A. R. (2025) · Journal of Visual Literacy · doi:10.1080/1051144x.2025.2543687 · Cited by 3 · see citing works
Hellmann, O. (2026) · Kōtuitui New Zealand Journal of Social Sciences Online · doi:10.1002/kot2.70016 · Cited by 3 · see citing works
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“Previous research has highlighted challenges for image generators to depict PwD in respectful and realistic ways [48, 77], echoing broader biases seen in representations of other marginalized communities [12, 21, 81, 91].”
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Cheong, M., Bush, G., & Wildenauer, M. (2025) · AI and Ethics · doi:10.1007/s43681-025-00671-2
Self-citationMedienregulierung und die Zukunft der Medien: Das Fallbeispiel künstliche Intelligenz
Daubs, M. (2025) · Medien, Kultur, Kommunikation · doi:10.1007/978-3-658-47029-6_11
Visual Entrepreneurship Stereotypes Exploring Ai-Driven Image Generation with MidjourneyPreprint
Hormiga, E., Jonckers, G., & Urbano, D. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5370417
Mondragón, J. J. P., & Trujillo, A. E. (2025) · Cureus Journal of Computer Science. · doi:10.7759/s44389-025-07546-x
Can AI-generated faces serve as fillers in eyewitness lineups?Preprint
Bergold, A. N., & Greenspan, R. L. (2024) · OSF Preprints (OSF Preprints) · doi:10.17605/osf.io/k8jaf
Decoding AI Narratives: Unveiling Linguistic Bias in AI Image Metadata and Its Impact
Wang, J., & Gwebu, K. L. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-10721-3_41
The Image of the Academic Community in Generative AI Models: A Case Study of Flux.1
Wałczyk, K., & Maszybrocka, J. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-04197-5_26
Analyzing Recursiveness in Multimodal Generative Artificial Intelligence: Stability or Divergence?
Conde, J., Cheung, T., Martínez, G., Reviriego, P., & Sarkar, R. (2025) · International Conference on Foundation and Large Language Models (FLLM) · doi:10.1109/fllm67465.2025.11391196
STEM professionals and teacher perception in images created with ChatGPT and DALL-E
Gülhan, F. (2026) · Discover Education · doi:10.1007/s44217-026-01439-4
Wölfle, A., & Schmuck, D. (2026) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2026.2685242
AI-generated faces are becoming more trustworthy
McGuire, A., Boháček, M., Farid, H., Taylor, P. J., & Nightingale, S. J. (2026) · Journal of Vision · doi:10.1167/jov.26.7.3
LangBiTe: model-driven bias testing of text-to-text large language models
Morales, S., Clarisó, R., & Cabot, J. (2026) · Software & Systems Modeling · doi:10.1007/s10270-026-01425-2
Mahl, D., & Guenther, L. (2026) · Information Communication & Society · doi:10.1080/1369118x.2026.2740745
Black technofeminism as critique of biological essentialism in AI anthropomorphization
Brown, M. C. (2026) · Information Communication & Society · doi:10.1080/1369118x.2026.2752104
- Authors
- Cheong, M., Abedin, E., Ferreira, M., Reimann, R., Chalson, S., Robinson, P., Byrne, J., Ruppanner, L., Alfano, M., & Klein, C.
- Affiliations
- The University of Melbourne (AU)
- Macquarie University (AU)
- Australian National University (AU)
- ORCID
- Venue
- ACM Journal on Responsible Computing
- Published
- 2024-06-20
- Volume
- 1
- Issue
- 2
- Pages
- 1-20
- ISSN
- 2832-0565 (ISSN-L)
- OpenAlex
- W4392385810
- Cited by
- 57
- References
- 47
- Citations by year
- 2026: 13 · 2025: 31 · 2024: 11 · 2023: 2
- FWCI
- 22.90
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- ACM Journal on Responsible Computing (publishedVersion, cc-by)
- PhilPapers (PhilPapers Foundation) (submittedVersion)
- Topics
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Physical Sciences › Computer Science › Artificial Intelligence › Law, AI, and Intellectual Property
- Keywords
- gender bias, racial bias, generative image models, occupational stereotypes, AI regulation
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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